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| import streamlit as st | |
| from st_audiorec import st_audiorec | |
| from Modules.Speech2Text.transcribe import transcribe | |
| import base64 | |
| from langchain_mistralai import ChatMistralAI | |
| from langchain_core.prompts import ChatPromptTemplate | |
| from dotenv import load_dotenv | |
| load_dotenv() # load .env api keys | |
| import os | |
| from Modules.rag import rag_chain | |
| from Modules.router import router_chain | |
| # from Modules.PoseEstimation.pose_agent import agent_executor | |
| mistral_api_key = os.getenv("MISTRAL_API_KEY") | |
| from Modules.PoseEstimation import pose_estimator | |
| from utils import save_uploaded_file | |
| def format_messages(messages): | |
| formatted_messages = "" | |
| for message in messages: | |
| role = message["role"] | |
| content = message["content"] | |
| formatted_messages += f"{role}: {content}\n" | |
| return formatted_messages | |
| st.set_page_config(layout="wide", initial_sidebar_state="collapsed") | |
| # Create two columns | |
| col1, col2 = st.columns(2) | |
| video_uploaded = None | |
| llm = ChatMistralAI(model="mistral-large-latest", mistral_api_key=mistral_api_key, temperature=0) | |
| prompt = ChatPromptTemplate.from_template( | |
| template =""" You are a personal AI sports coach with an expertise in nutrition and fitness. | |
| You are having a conversation with your client, which is either a beginner or an advanced athlete. | |
| You must be gentle, kind, and motivative. | |
| Always try to answer concisely to the queries. | |
| User: {question} | |
| AI Coach:""" | |
| ) | |
| base_chain = prompt | llm | |
| # First column containers | |
| with col1: | |
| st.subheader("Audio Recorder") | |
| recorded = False | |
| temp_path = 'data/temp_audio/audio_file.wav' | |
| wav_audio_data = st_audiorec() | |
| if wav_audio_data is not None: | |
| with open(temp_path, 'wb') as f: | |
| # Write the audio data to the file | |
| f.write(wav_audio_data) | |
| instruction = transcribe(temp_path) | |
| print(instruction) | |
| recorded = True | |
| st.subheader("LLM answering") | |
| if recorded: | |
| if "messages" not in st.session_state: | |
| st.session_state.messages = [] | |
| for message in st.session_state.messages: | |
| with st.chat_message(message["role"]): | |
| st.markdown(message["content"]) | |
| st.session_state.messages.append({"role": "user", "content": instruction}) | |
| with st.chat_message("user"): | |
| st.markdown(instruction) | |
| with st.chat_message("assistant"): | |
| # Build answer from LLM | |
| direction = router_chain.invoke({"question":instruction}) | |
| if direction=='fitness_advices': | |
| response = rag_chain.invoke( | |
| instruction | |
| ) | |
| elif direction=='smalltalk': | |
| response = base_chain.invoke( | |
| {"question":instruction} | |
| ).content | |
| # elif direction =='movement_analysis': | |
| # response = agent_executor.invoke( | |
| # {"input" : instruction} | |
| # )["output"] | |
| print(type(response)) | |
| st.session_state.messages.append({"role": "assistant", "content": response}) | |
| st.markdown(response) | |
| st.subheader("Movement Analysis") | |
| # TO DO | |
| # Second column containers | |
| with col2: | |
| st.subheader("Sports Agenda") | |
| # TO DO | |
| st.subheader("Video Analysis") | |
| ask_video = st.empty() | |
| if video_uploaded is None: | |
| video_uploaded = ask_video.file_uploader("Choose a video file", type=["mp4", "ogg", "webm"]) | |
| if video_uploaded: | |
| video_uploaded = save_uploaded_file(video_uploaded) | |
| ask_video.empty() | |
| _left, mid, _right = st.columns(3) | |
| with mid: | |
| st.video(video_uploaded) | |
| apply_pose = st.button("Apply Pose Estimation") | |
| if apply_pose: | |
| with st.spinner("Processing video"): | |
| keypoints = pose_estimator.get_keypoints_from_keypoints(pose_estimator.model, video_uploaded) | |
| st.subheader("Graph Displayer") | |
| # TO DO |